Adept traffic models are critical to both real-time prediction/planning and closed-loop simulation for autonomous vehicles (AV). Key design objectives include accuracy, diverse multimodal behaviors, interpretability, and compatibility with other modules in the autonomy stack, e.g., the downstream planner. We present Categorical Traffic Transformer (CTT), a traffic model that outputs both continuous trajectory predictions and categorical predictions with clear semantic meanings (lane modes, homotopies, etc.). The most outstanding feature of CTT is its fully interpretable latent space, which enables direct supervision of the latent variables from the ground truth during training and avoids mode collapse completely. As a result, CTT can generate diverse behaviors conditioned on different semantic modes while significantly beating SOTA on prediction accuracy. In addition, CTT's ability to input and output tokens enables direct integration with semantic-heavy modules such as behavior planners and language models, bridging the tokenized representation and the continuous trajectory space.

Safety filters based on Control Barrier Functions (CBFs) have emerged as a practical tool for the safety-critical control of autonomous systems. These approaches encode safety through a value function and enforce safety by imposing a constraint on the time derivative of this value function. How-ever, synthesizing a valid CBF that is not overly conservative in the presence of input constraints is a notorious challenge. In this work, we propose refining a candidate CBF using formal verification methods to obtain a valid CBF. In particular, we update an expert-synthesized or backup CBF using dynamic programming (DP) based reachability analysis. Our framework, REFINECBF, guarantees that with every DP iteration the obtained CBF is provably at least as safe as the prior iteration and converges to a valid CBF. Therefore, REFINECBF can be used in-the-loop for robotic systems. We demonstrate the practicality of our method to enhance safety and/or reduce conservativeness on a range of nonlinear control-affine systems using various CBF synthesis techniques in simulation.

Finite element methods have been successfully used to develop physics-based models of soft robots that capture the nonlinear dynamic behavior induced by continuous deformation. These high-fidelity models are therefore ideal for designing controllers for complex dynamic tasks such as trajectory optimization and trajectory tracking. However, finite element models are also typically very high-dimensional, which makes real-time control challenging. In this work we propose an approach for finite element model-based control of soft robots that leverages model order reduction techniques to significantly increase computational efficiency. In particular, a constrained optimal control problem is formulated based on a nonlinear reduced order finite element model and is solved via sequential convex programming. This approach is demonstrated through simulation of a cable-driven soft robot for a constrained trajectory tracking task, where a 9768-dimensional finite element model is used for controller design.